Bhattacharyya distance-based tracking: A vehicle counting application

Baskoro, Jatmiko Budi; Wibisono, Ari; Jatmiko, Wisnu

doi:10.1109/icacsis.2017.8355071

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…In [109], the Bhattacharyya Distance is used to calculate changes of color histogram. In [112], Baskoro et al proposed an algorithm for counting and tracking vehicles using the Bhattacharyya Distance. It is used by Laz in [113] to evaluate detection system performance.…”

Section: ) Bhattacharyya Distancementioning

confidence: 99%

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A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

et al. 2022

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Internet traffic classification aims to identify the kind of Internet traffic. With the rise of traffic encryption and multi-layer data encapsulation, some classic classification methods have lost their strength. In an attempt to increase classification performance, Machine Learning (ML) strategies have gained the scientific community interest and have shown themselves promising in the future of traffic classification, mainly in the recognition of encrypted traffic. However, some of these methods have a high computational resource consumption, which make them unfeasible for classification of large traffic flows or in real-time. Methods using statistical analysis have been used to classify real-time traffic or large traffic flows, where the main objective is to find statistical differences among flows or find a pattern in traffic characteristics through statistical properties that allow traffic classification. The purpose of this work is to address statistical methods to classify Internet traffic that were little or unexplored in the literature. This work is not generally focused on discussing statistical methodology. It focuses on discussing statistical tools applied to Internet traffic classification Thus, we provide an overview on statistical distances and divergences previously used or with potential to be used in the classification of Internet traffic. Then, we review previous works about Internet traffic classification using statistical methods, namely Euclidean, Bhattacharyya, and Hellinger distances, Jensen-Shannon and Kullback-Leibler (KL) divergences, Support Vector Machines (SVM), Correlation Information (Pearson Correlation), Kolmogorov-Smirnov and Chi-Square tests, and Entropy. We also discuss some open issues and future research directions on Internet traffic classification using statistical methods.

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Section: ) Bhattacharyya Distancementioning

confidence: 99%

“…N/A Baskoro et al [112] 2017 Features: Probability Density Function (PDF), number of pixel, and color pdf's. P:96.5, R:96.3 Laz [113] 2017 Techniques: lagrange multipliers technique, and parallel computing.…”

Section: N/amentioning

confidence: 99%

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

et al. 2022

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“…An interesting application to computer vision problems was studied in [6], where Bhattacharyya distance (i.e. exponent of Renyi divergence with ↵ = 1 2 ) was used for vehicle tracking and counting.…”

Section: Related Workmentioning

confidence: 99%

Generalization of Cross-Entropy Loss Function for Image Classification

Andreieva

Shvai

2021

MMJ

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Classification task is one of the most common tasks in machine learning. This supervised learning problem consists in assigning each input to one of a finite number of discrete categories. Classification task appears naturally in numerous applications, such as medical image processing, speech recognition, maintenance systems, accident detection, autonomous driving etc.In the last decade methods of deep learning have proven to be extremely efficient in multiple machine learning problems, including classification. Whereas the neural network architecture might depend a lot on data type and restrictions posed by the nature of the problem (for example, real-time applications), the process of its training (i.e. finding model’s parameters) is almost always presented as loss function optimization problem.Cross-entropy is a loss function often used for multiclass classification problems, as it allows to achieve high accuracy results.Here we propose to use a generalized version of this loss based on Renyi divergence and entropy. We remark that in case of binary labels proposed generalization is reduced to cross-entropy, thus we work in the context of soft labels. Specifically, we consider a problem of image classification being solved by application of convolution neural networks with mixup regularizer. The latter expands the training set by taking convex combination of pairs of data samples and corresponding labels. Consequently, labels are no longer binary (corresponding to single class), but have a form of vector of probabilities. In such settings cross-entropy and proposed generalization with Renyi divergence and entropy are distinct, and their comparison makes sense.To measure effectiveness of the proposed loss function we consider image classification problem on benchmark CIFAR-10 dataset. This dataset consists of 60000 images belonging to 10 classes, where images are color and have the size of 32×32. Training set consists of 50000 images, and the test set contains 10000 images.For the convolution neural network, we follow [1] where the same classification task was studied with respect to different loss functions and consider the same neural network architecture in order to obtain comparable results.Experiments demonstrate superiority of the proposed method over cross-entropy for loss function parameter value α < 1. For parameter value α > 1 proposed method shows worse results than cross-entropy loss function. Finally, parameter value α = 1 corresponds to cross-entropy.

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Bhattacharyya distance-based tracking: A vehicle counting application

Cited by 2 publications

References 19 publications

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

Generalization of Cross-Entropy Loss Function for Image Classification

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